Wet Day#
import warnings
warnings.filterwarnings("ignore")
import os
import sys
import folium
import numpy as np
import pandas as pd
sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive
from ind_setup.colors import get_df_col
from ind_setup.core import fontsize
sys.path.append("../../../functions")
from data_downloaders import GHCN
country = 'Palau'
vars_interest = ['PRCP']
Get Data#
df_country = GHCN.get_country_code(country)
print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')
The GHCN code for Palau is PS
df_stations = GHCN.download_stations_info()
df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
print(f'There are {df_country_stations.shape[0]} stations in {country}')
There are 13 stations in Palau
GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
for var in vars_interest:
globals()[f"dict_{var}"], IDS = GHCN.extract_dict_data_var(GHCND_dir, var, df_country_stations)
for var in vars_interest:
dict_plot = globals()[f'dict_{var}']
fig = plot_timeseries_interactive(dict_plot, trendline=False, ylims = [None, None])
Using Koror Station#
Analysis of wet days
id = 'PSW00040309' # Koror Station
dict_prcp = GHCN.extract_dict_data_var(GHCND_dir, 'PRCP', df_country_stations.loc[df_country_stations['ID'] == id])[0]
fig = plot_timeseries_interactive(dict_prcp, trendline=True, ylims = [None, None])
Wet days#
data = dict_prcp[0]['data']#.dropna()
data = data.groupby(data.index.year).filter(lambda x: len(x) >= 300).dropna()
data['wet_day'] = np.where(data['PRCP'] > 0, 1, np.where((np.isnan(data['PRCP'])==True), np.nan, 0))
import matplotlib.pyplot as plt
# Create the histogram
def plot_bar_probs(x, y, labels = None, figsize = [7, 5]):
"""
Plots a bar chart showing the distribution of wet days.
Parameters:
x (list): The x-axis values for the bar chart.
y (list): The y-axis values for the bar chart.
labels (list, optional): The labels for the x-axis ticks. Defaults to None.
Returns:
None
"""
fig, ax = plt.subplots(figsize = figsize)
ax.bar(x = x, height = y, color=get_df_col()[0], edgecolor='white', alpha = .5)
ax.set_ylabel('Frequency', fontsize = fontsize)
if labels:
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize = fontsize)
ax.grid(color = 'lightgrey', linestyle = ':', alpha = 0.6)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
return ax
ax = plot_bar_probs(x = [0, 1], y = data.groupby('wet_day').count()['PRCP'].values, labels = ['Dry Days', 'Wet Days'])
ax.set_title('Distribution of Wet Days', fontsize = fontsize)
Text(0.5, 1.0, 'Distribution of Wet Days')
Accumulated precipitation#
# Correct accumulated precipitation with number of observations per year to make fair comparisons and trends
datag = (data.groupby(data.index.year).sum()/ data.groupby(data.index.year).count()) * 365
datag.index = pd.to_datetime(datag.index, format = '%Y')
dict_plot = [{'data' : datag, 'var' : 'PRCP', 'ax' : 1, 'label':'Accumulated precipitation [mm]'},]
plot_timeseries_interactive(dict_plot, trendline = True);
ax = plot_bar_probs(x = datag.index.year, y = datag['PRCP'].values, figsize = [15, 4])
ax.set_title('Accumulated Rainfall Over Time', fontsize = fontsize)
Text(0.5, 1.0, 'Accumulated Rainfall Over Time')
Number of days over and above threshold#
threshold = 1 #np.percentile(data['PRCP'].dropna(), 90)
data['wet_day_t'] = np.where(data['PRCP'] > threshold, 1, np.where((np.isnan(data['PRCP'])==True), np.nan, 0))
data_th = data.groupby([data.index.year, data.wet_day_t]).count()['PRCP']
data_th = data_th/data.groupby(data.index.year).count()['PRCP'] * 365
fig, ax = plt.subplots(figsize = [15, 5])
data_th.unstack().plot(kind = 'bar', stacked = True, ax = ax, color = get_df_col()[:2], edgecolor = 'white', alpha = .5)
ax.set_ylabel('Number of days', fontsize = fontsize)
Text(0, 0.5, 'Number of days')
Days over threshold#
threshold = np.round(np.percentile(data['PRCP'].dropna(), 95), 2)
print(f'Threshold of {threshold}mm')
data['wet_day_t'] = np.where(data['PRCP'] > threshold, 1, np.where((np.isnan(data['PRCP'])==True), np.nan, 0))
Threshold of 45.7mm
data_2 = data.loc[data['wet_day_t'] == 1][['PRCP']]
data_over_th = data_2.groupby(data_2.index.year).count()
data_over_th.index = pd.to_datetime(data_over_th.index, format = '%Y')
data_over_th['PRCP_below'] = 365 - data_over_th['PRCP'].values
dict_plot = [{'data' : data_over_th, 'var' : 'PRCP', 'ax' : 1, 'label':f'Number of days over threshold: {threshold}mm'},]
plot_timeseries_interactive(dict_plot, trendline = True);
dict_plot = [{'data' : data_over_th, 'var' : 'PRCP_below', 'ax' : 1, 'label':f'Number of days below threshold: {threshold}mm'}]
plot_timeseries_interactive(dict_plot, trendline = True);